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            <h1 id="seo-header">Seurat (三) scRNA-seq数据全流程分析 (二)</h1>
            
            
              <div class="markdown-body">
                
                <h2 id="第五步-手动标注-一-marker表达可视化"><a href="#第五步-手动标注-一-marker表达可视化" class="headerlink" title="第五步 手动标注 (一) marker表达可视化"></a>第五步 手动标注 (一) marker表达可视化</h2><figure class="highlight lua"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br><span class="line">60</span><br><span class="line">61</span><br><span class="line">62</span><br><span class="line">63</span><br><span class="line">64</span><br><span class="line">65</span><br><span class="line">66</span><br><span class="line">67</span><br><span class="line">68</span><br><span class="line">69</span><br><span class="line">70</span><br><span class="line">71</span><br><span class="line">72</span><br><span class="line">73</span><br><span class="line">74</span><br><span class="line">75</span><br><span class="line">76</span><br><span class="line">77</span><br><span class="line">78</span><br><span class="line">79</span><br><span class="line">80</span><br><span class="line">81</span><br><span class="line">82</span><br><span class="line">83</span><br><span class="line">84</span><br><span class="line">85</span><br><span class="line">86</span><br><span class="line">87</span><br><span class="line">88</span><br><span class="line">89</span><br><span class="line">90</span><br><span class="line">91</span><br><span class="line">92</span><br><span class="line">93</span><br><span class="line">94</span><br><span class="line">95</span><br><span class="line">96</span><br><span class="line">97</span><br><span class="line">98</span><br><span class="line">99</span><br><span class="line">100</span><br><span class="line">101</span><br><span class="line">102</span><br><span class="line">103</span><br><span class="line">104</span><br><span class="line">105</span><br><span class="line">106</span><br><span class="line">107</span><br><span class="line">108</span><br><span class="line">109</span><br><span class="line">110</span><br><span class="line">111</span><br><span class="line">112</span><br><span class="line">113</span><br><span class="line">114</span><br><span class="line">115</span><br><span class="line">116</span><br><span class="line">117</span><br><span class="line">118</span><br><span class="line">119</span><br><span class="line">120</span><br><span class="line">121</span><br><span class="line">122</span><br></pre></td><td class="code"><pre><code class="hljs lua"># <span class="hljs-number">4</span>、构造图片保存方法<br>f_image_output &lt;- <span class="hljs-function"><span class="hljs-keyword">function</span><span class="hljs-params">(fileName, image, width=1920, height=1080, lc_pdf=T, lc_resolution=72)</span></span>&#123;<br>    <span class="hljs-keyword">if</span>(lc_pdf)&#123;<br>        width = width / lc_resolution<br>        height = height / lc_resolution<br>        pdf(paste(fileName, <span class="hljs-string">&quot;.pdf&quot;</span>, sep=<span class="hljs-string">&quot;&quot;</span>), width = width, height = height)<br>    &#125;<span class="hljs-keyword">else</span>&#123;<br>        png(paste(fileName, <span class="hljs-string">&quot;.png&quot;</span>, sep=<span class="hljs-string">&quot;&quot;</span>), width = width, height = height)<br>    &#125;<br>    <span class="hljs-built_in">print</span>(image)<br>    dev.off()<br>&#125;<br><br># <span class="hljs-number">14</span>、划分细胞群<br>scRNA &lt;- FindNeighbors(scRNA, dims = <span class="hljs-number">1</span>:pca_dim)<br>scRNA &lt;- FindClusters(scRNA, resolution = <span class="hljs-number">0.5</span>)<br>DimPlot(scRNA, reduction = lc_reduction)<br><br># <span class="hljs-number">16</span>、寻找显著基因<br>all_markers &lt;- FindAllMarkers(scRNA, only.pos = TRUE, <span class="hljs-built_in">min</span>.pct = <span class="hljs-number">0.25</span>, logfc.threshold = <span class="hljs-number">0.25</span>)<br>significant_markers &lt;- subset(all_markers, subset = p_val_adj&lt;<span class="hljs-number">0.05</span>)<br><span class="hljs-built_in">write</span>.csv(significant_markers, <span class="hljs-string">&quot;significant_markers.csv&quot;</span>)<br><br>f_cluster_averages &lt;- <span class="hljs-function"><span class="hljs-keyword">function</span><span class="hljs-params">(lc_scRNA)</span></span>&#123;<br>    # 切分出Clusters<br>    lc_clusters &lt;- SplitObject(lc_scRNA, split.by = <span class="hljs-string">&#x27;ident&#x27;</span>)<br>    <span class="hljs-keyword">for</span> (lc_i <span class="hljs-keyword">in</span> <span class="hljs-number">1</span>:length(lc_clusters))&#123;<br>        lc_clusters<span class="hljs-string">[[lc_i]]</span>  &lt;- lc_clusters<span class="hljs-string">[[lc_i]]</span><span class="hljs-string">[[lc_clusters[[lc_i]]@active.assay]]</span>@scale.data<br>    &#125;<br>    <span class="hljs-keyword">for</span> (lc_i <span class="hljs-keyword">in</span> <span class="hljs-number">1</span>:length(lc_clusters))&#123;<br>        lc_clusters<span class="hljs-string">[[lc_i]]</span>  &lt;- apply(lc_clusters<span class="hljs-string">[[lc_i]]</span>,<span class="hljs-number">1</span>,mean)<br>    &#125;<br>    lc_clusters &lt;- data.frame(lc_clusters)<br>    scale(lc_clusters)<br>&#125;<br>library(Hmisc)<br>library(corrplot)<br>f_corrplot &lt;- <span class="hljs-function"><span class="hljs-keyword">function</span><span class="hljs-params">(lc_scdata)</span></span>&#123;<br>    lc_cor &lt;- rcorr(lc_scdata)<br>    lc_cor$P[is.na(lc_cor$P)]=<span class="hljs-number">0</span><br>    lc_cor$P &lt;- -<span class="hljs-built_in">log10</span>(lc_cor$P)<br>    lc_cor$P[is.infinite(lc_cor$P)] = <span class="hljs-built_in">max</span>(lc_cor$P[!is.infinite(lc_cor$P)]) + <span class="hljs-number">1</span><br>    corrplot(lc_cor$r, <span class="hljs-built_in">type</span>=<span class="hljs-string">&quot;upper&quot;</span>,tl.pos = <span class="hljs-string">&quot;d&quot;</span>,order = <span class="hljs-string">&quot;AOE&quot;</span>, p.mat = lc_cor$P, insig = <span class="hljs-string">&quot;p-value&quot;</span>)<br>    corrplot(lc_cor$r, add=TRUE, <span class="hljs-built_in">type</span>=<span class="hljs-string">&quot;lower&quot;</span>, method=<span class="hljs-string">&quot;number&quot;</span>,order = <span class="hljs-string">&quot;AOE&quot;</span>, diag=FALSE,tl.pos=<span class="hljs-string">&quot;n&quot;</span>, cl.pos=<span class="hljs-string">&quot;n&quot;</span>)<br>&#125;<br><br># <span class="hljs-number">3</span>、准备好新的marker genes list<br>gl_MarkerGene &lt;- <span class="hljs-built_in">read</span>.csv(file.<span class="hljs-built_in">path</span>(root_path, <span class="hljs-string">&quot;cellType_Markers.csv&quot;</span>), header = F)<br>colnames(gl_MarkerGene) &lt;- c(<span class="hljs-string">&#x27;marker&#x27;</span>,<span class="hljs-string">&#x27;celltype&#x27;</span>)<br><br>gl_Markers_ExN &lt;- <span class="hljs-built_in">read</span>.<span class="hljs-built_in">table</span>(file.<span class="hljs-built_in">path</span>(root_path, <span class="hljs-string">&quot;ExN.csv&quot;</span>), header = F)<br>colnames(gl_Markers_ExN) &lt;- c(<span class="hljs-string">&#x27;marker&#x27;</span>,<span class="hljs-string">&#x27;celltype&#x27;</span>)<br><br>gl_Markers_InN &lt;- <span class="hljs-built_in">read</span>.<span class="hljs-built_in">table</span>(file.<span class="hljs-built_in">path</span>(root_path, <span class="hljs-string">&quot;InN.csv&quot;</span>), header = F)<br>colnames(gl_Markers_InN) &lt;- c(<span class="hljs-string">&#x27;marker&#x27;</span>,<span class="hljs-string">&#x27;celltype&#x27;</span>)<br><br>gl_TS3 &lt;- <span class="hljs-built_in">read</span>.csv(file.<span class="hljs-built_in">path</span>(root_path, <span class="hljs-string">&quot;TableS3.csv&quot;</span>))<br>colnames(gl_TS3) &lt;- c(<span class="hljs-string">&#x27;marker&#x27;</span>,<span class="hljs-string">&#x27;celltype&#x27;</span>)<br>gl_TS3$celltype &lt;- substr(gl_TS3$celltype,<span class="hljs-number">1</span>,<span class="hljs-number">3</span>)<br><br>f_setRowName &lt;- <span class="hljs-function"><span class="hljs-keyword">function</span><span class="hljs-params">(lc_df, lc_colName)</span></span>&#123;<br>    lc_df &lt;- lc_df[order(lc_df<span class="hljs-string">[[lc_colName]]</span>),]<br>    tp_index &lt;- duplicated(lc_df<span class="hljs-string">[[lc_colName]]</span>)<br>    lc_df &lt;- lc_df[!tp_index,]<br>    rownames(lc_df) &lt;- lc_df<span class="hljs-string">[[lc_colName]]</span><br>    lc_df<br>&#125;<br><br>gl_MarkerGene &lt;- f_setRowName(gl_MarkerGene, <span class="hljs-string">&#x27;marker&#x27;</span>)<br>gl_TS3 &lt;- f_setRowName(gl_TS3, <span class="hljs-string">&#x27;marker&#x27;</span>)<br>gl_Markers_ExN &lt;- f_setRowName(gl_Markers_ExN, <span class="hljs-string">&#x27;marker&#x27;</span>)<br>gl_Markers_InN &lt;- f_setRowName(gl_Markers_InN, <span class="hljs-string">&#x27;marker&#x27;</span>)<br><br># <span class="hljs-number">18</span>、绘制已知基因在不同cluster内的Violin plots<br># <span class="hljs-number">18.1</span> 定义Violin plots绘制方法<br>f_get_cell_markers &lt;- <span class="hljs-function"><span class="hljs-keyword">function</span><span class="hljs-params">(lc_cellType, lc_significant_markers, lc_MarkerGene)</span></span>&#123;<br>    lc_features &lt;- rownames(subset(lc_MarkerGene, subset = celltype == lc_cellType))<br>    lc_features &lt;- lc_features[lc_features %<span class="hljs-keyword">in</span>% lc_significant_markers$gene]<br>    lc_features<br>&#125;<br>f_VlnPlot &lt;- <span class="hljs-function"><span class="hljs-keyword">function</span><span class="hljs-params">(sObject, lc_cellType, lc_significant_markers, lc_MarkerGene)</span></span> &#123;<br>    lc_features &lt;- f_get_cell_markers(lc_cellType, lc_significant_markers, lc_MarkerGene)<br>    <span class="hljs-keyword">if</span> (length(lc_features)!=<span class="hljs-number">0</span>)&#123;VlnPlot(sObject, features = lc_features)&#125;<br>    <span class="hljs-keyword">else</span>&#123; F &#125; <br>&#125;<br><br>f_get_m_p &lt;- <span class="hljs-function"><span class="hljs-keyword">function</span><span class="hljs-params">(sObject, lc_cellType, lc_significant_markers, lc_MarkerGene)</span></span>&#123;<br>    lc_features &lt;- f_get_cell_markers(lc_cellType, lc_significant_markers, lc_MarkerGene)<br>    <span class="hljs-keyword">if</span> (length(lc_features)==<span class="hljs-number">0</span>)&#123;<span class="hljs-keyword">return</span>(NULL)&#125;<br>    DotPlot(sObject, features = lc_features) + RotatedAxis() + <br>    ggtitle(sprintf(<span class="hljs-string">&quot;%s markers in %s&quot;</span>, lc_cellType, sObject@project.name)) + <br>    theme(plot.title = element_text(hjust = <span class="hljs-number">0.5</span>))<br>&#125;<br><br># <span class="hljs-number">18.2</span> 绘制已知细胞类型的marker genes在本数据集中的pattern<br>f_get_all_celltype &lt;- <span class="hljs-function"><span class="hljs-keyword">function</span><span class="hljs-params">(lc_MarkerGene)</span></span>&#123;<br>    tp_cellTypes &lt;- lc_MarkerGene$celltype # 获取所有细胞类型<br>    tp_cellTypes &lt;- <span class="hljs-built_in">sort</span>(tp_cellTypes) # 排序<br>    tp_cellTypes &lt;- tp_cellTypes[!duplicated(tp_cellTypes)] #去重<br>    tp_cellTypes<br>&#125;<br><br>f_get_m_p_a &lt;- <span class="hljs-function"><span class="hljs-keyword">function</span><span class="hljs-params">(sObject, lc_significant_markers, lc_MarkerGene)</span></span>&#123;<br>    lc_cellTypes &lt;- f_get_all_celltype(lc_MarkerGene)<br>    tp_emm &lt;- <span class="hljs-number">2</span><br>    <span class="hljs-keyword">for</span> (lc_j <span class="hljs-keyword">in</span> <span class="hljs-number">1</span>:length(lc_cellTypes))&#123;<br>        lc_image &lt;- f_get_m_p(sObject, lc_cellTypes[lc_j], lc_significant_markers, lc_MarkerGene)<br>        <span class="hljs-keyword">if</span> (!is.null(lc_image))&#123; <span class="hljs-keyword">break</span> &#125;<br>        tp_emm  &lt;- tp_emm + <span class="hljs-number">1</span><br>    &#125;<br>    <br>    <span class="hljs-keyword">if</span> (tp_emm &gt; length(lc_cellTypes))&#123;<span class="hljs-keyword">return</span>(NULL)&#125;<br>    <br>    <span class="hljs-keyword">for</span>(lc_j <span class="hljs-keyword">in</span> tp_emm:length(lc_cellTypes))&#123;<br>        lc_image &lt;- lc_image + f_get_m_p(sObject, lc_cellTypes<span class="hljs-string">[[lc_j]]</span>, lc_significant_markers, lc_MarkerGene)<br>    &#125;<br>    lc_image &lt;- lc_image + plot_layout(ncol = <span class="hljs-number">3</span>)<br>    lc_image<br>&#125;<br><br>tp_image &lt;- f_get_m_p_a(scRNA, significant_markers, gl_MarkerGene)<br>f_image_output(<span class="hljs-string">&#x27;gl_MarkerGene&#x27;</span>,tp_image, width = <span class="hljs-number">2160</span>, height = <span class="hljs-number">2160</span>)<br></code></pre></td></tr></table></figure>

<p><a target="_blank" rel="noopener" href="https://img-cdn.limour.top/blog_wp/2021/10/gl_MarkerGene.png"><img src="https://img-cdn.limour.top/blog_wp/2021/10/gl_MarkerGene.png" srcset="https://jscdn.limour.top/gh/Limour-dev/Sakurairo_Vision/load_svg/inload.svg" lazyload></a></p>
<h2 id="第六步-手动标注-二-生成默认标注"><a href="#第六步-手动标注-二-生成默认标注" class="headerlink" title="第六步 手动标注 (二) 生成默认标注"></a>第六步 手动标注 (二) 生成默认标注</h2><figure class="highlight reasonml"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br></pre></td><td class="code"><pre><code class="hljs reasonml"># <span class="hljs-number">19.1</span> 定义产生空标注的方法<br>f_unkown_marker_list &lt;- <span class="hljs-keyword">function</span>(sObject)&#123;<br>    tp_celltype  &lt;- <span class="hljs-built_in">list</span><span class="hljs-literal">()</span><br>    <span class="hljs-keyword">for</span> (lc_i <span class="hljs-keyword">in</span> <span class="hljs-number">1</span>:length(levels(sObject)))&#123;<br>        tp_celltype<span class="hljs-literal">[[<span class="hljs-identifier">lc_i</span>]</span>] &lt;- sprintf(<span class="hljs-string">&quot;Unkown_%02d&quot;</span>, lc_i)<br>    &#125;<br>    tp_celltype<br>&#125;<br>f_marker_list_tp &lt;- <span class="hljs-keyword">function</span>(lc_significant_markers, lc_MarkerGene)&#123;<br>    tp_cluster_markers &lt;- lc_MarkerGene<span class="hljs-literal">[<span class="hljs-identifier">lc_significant_markers$gene</span>,]</span><br>    tp_id &lt;- which(row<span class="hljs-constructor">Sums(<span class="hljs-params">is</span>.<span class="hljs-params">na</span>(<span class="hljs-params">tp_cluster_markers</span>)</span>)<span class="hljs-operator"> == </span><span class="hljs-number">0</span>)<br>    lc_celltype &lt;- tp_cluster_markers<span class="hljs-literal">[,<span class="hljs-number">2</span>]</span><br>    tp_cluster_markers &lt;- cbind(lc_significant_markers, lc_celltype)<br>    tp_cluster_markers &lt;- tp_cluster_markers<span class="hljs-literal">[<span class="hljs-identifier">tp_id</span>,]</span><br>    tp_cluster_markers<br>&#125;<br># <span class="hljs-number">19.2</span> 定义产生标注的方法<br>f_marker_list &lt;- <span class="hljs-keyword">function</span>(sObject, lc_significant_markers, lc_MarkerGene)&#123;<br>    lc_celltypes &lt;- f<span class="hljs-constructor">_marker_list_tp(<span class="hljs-params">lc_significant_markers</span>, <span class="hljs-params">lc_MarkerGene</span>)</span><span class="hljs-literal">[,<span class="hljs-identifier">c</span>(&quot;<span class="hljs-identifier">cluster</span>&quot;,&quot;<span class="hljs-identifier">lc_celltype</span>&quot;)]</span><br>    lc_celltypes &lt;- lc_celltypes<span class="hljs-literal">[<span class="hljs-identifier">which</span>(!<span class="hljs-identifier">duplicated</span>(<span class="hljs-identifier">lc_celltypes</span>[,<span class="hljs-number">1</span>]</span>)),]<br>    #print(lc_celltypes)<br>    r_celltypes &lt;- f<span class="hljs-constructor">_unkown_marker_list(<span class="hljs-params">sObject</span>)</span><br>    <span class="hljs-keyword">for</span> (lc_i <span class="hljs-keyword">in</span> <span class="hljs-number">1</span>:nrow(lc_celltypes))&#123;<br>        r_celltypes<span class="hljs-literal">[[<span class="hljs-identifier">as</span>.<span class="hljs-identifier">integer</span>(<span class="hljs-identifier">lc_celltypes</span>[<span class="hljs-identifier">lc_i</span>,<span class="hljs-number">1</span>]</span>)]] &lt;- lc_celltypes<span class="hljs-literal">[<span class="hljs-identifier">lc_i</span>,<span class="hljs-number">2</span>]</span><br>    &#125;<br>    r_celltypes<br>&#125;<br><br>f_setDistinction &lt;- <span class="hljs-keyword">function</span>(sObject, lc_filename, lc_significant_markers, lc_MarkerGene)&#123;<br>    <span class="hljs-keyword">if</span>(!file.exists(lc_filename))&#123;<br>        write.csv(data.frame(cluster_ID=levels(sObject),<br>                ident_1=unlist(f<span class="hljs-constructor">_marker_list(<span class="hljs-params">sObject</span>, <span class="hljs-params">lc_significant_markers</span>, <span class="hljs-params">lc_MarkerGene</span>)</span>),<br>                ident_2=unlist(f<span class="hljs-constructor">_unkown_marker_list(<span class="hljs-params">sObject</span>)</span>)),<br>                lc_filename)<br>    &#125;<br>&#125;<br><br><span class="hljs-constructor">Idents(<span class="hljs-params">scRNA</span>)</span> &lt;- scRNA<span class="hljs-literal">[[&#x27;<span class="hljs-identifier">seurat_clusters</span>&#x27;]</span>]<br>f<span class="hljs-constructor">_setDistinction(<span class="hljs-params">scRNA</span>, <span class="hljs-string">&quot;Manual_distinction_1.csv&quot;</span>, <span class="hljs-params">significant_markers</span>, <span class="hljs-params">gl_MarkerGene</span>)</span><br><br># 请参考已知基因在不同cluster内的Violin plots进行修正，修正在ident_2<br># <span class="hljs-string">&quot;Manual_distinction_1.csv&quot;</span><br>f_marker_list_manual &lt;- <span class="hljs-keyword">function</span>(lc_filename)&#123;<br>    read.csv(lc_filename)<span class="hljs-literal">[,&quot;<span class="hljs-identifier">ident_2</span>&quot;]</span><br>&#125;<br><br>f_add_annotation &lt;- <span class="hljs-keyword">function</span>(sObject, lc_metaName, lc_filename)&#123;<br>    lc_ids &lt;- <span class="hljs-keyword">as</span>.vector(unlist(f<span class="hljs-constructor">_marker_list_manual(<span class="hljs-params">lc_filename</span>)</span>))<br>    names(lc_ids) &lt;- levels(sObject)<br>    sObject &lt;- <span class="hljs-constructor">RenameIdents(<span class="hljs-params">sObject</span>, <span class="hljs-params">lc_ids</span>)</span><br>    sObject<span class="hljs-literal">[[<span class="hljs-identifier">lc_metaName</span>]</span>] &lt;- <span class="hljs-constructor">Idents(<span class="hljs-params">sObject</span>)</span><br>    sObject<br>&#125;<br><br><span class="hljs-constructor">Idents(<span class="hljs-params">scRNA</span>)</span> &lt;- scRNA<span class="hljs-literal">[[&#x27;<span class="hljs-identifier">seurat_clusters</span>&#x27;]</span>]<br>scRNA &lt;- f<span class="hljs-constructor">_add_annotation(<span class="hljs-params">scRNA</span>, &#x27;<span class="hljs-params">manual_1</span>&#x27;, <span class="hljs-string">&quot;Manual_distinction_1.csv&quot;</span>)</span><br><br><span class="hljs-constructor">DimPlot(<span class="hljs-params">scRNA</span>, <span class="hljs-params">reduction</span> = <span class="hljs-params">lc_reduction</span>, <span class="hljs-params">group</span>.<span class="hljs-params">by</span> = &#x27;<span class="hljs-params">manual_1</span>&#x27;,  <span class="hljs-params">label</span> = T, <span class="hljs-params">repel</span> = T, <span class="hljs-params">label</span>.<span class="hljs-params">size</span> = 6)</span> + labs(title = <span class="hljs-string">&quot;UMAP reduction of clusters&quot;</span>)<br></code></pre></td></tr></table></figure>

<p><a target="_blank" rel="noopener" href="https://img-cdn.limour.top/blog_wp/2021/10/UMAP_manual.png"><img src="https://img-cdn.limour.top/blog_wp/2021/10/UMAP_manual.png" srcset="https://jscdn.limour.top/gh/Limour-dev/Sakurairo_Vision/load_svg/inload.svg" lazyload></a></p>
<h2 id="第七步-SingleR自动标注"><a href="#第七步-SingleR自动标注" class="headerlink" title="第七步 SingleR自动标注"></a>第七步 SingleR自动标注</h2><figure class="highlight stylus"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><code class="hljs stylus"><span class="hljs-function"><span class="hljs-title">saveRDS</span><span class="hljs-params">(scRNA, <span class="hljs-string">&quot;scRNA.rds&quot;</span>)</span></span><br></code></pre></td></tr></table></figure>

<p><a target="_blank" rel="noopener" href="https://limour.top/692.html">SingleR (五) 不同参考集与混合参考集的对比</a></p>
<h2 id="第八步-可视化对比"><a href="#第八步-可视化对比" class="headerlink" title="第八步 可视化对比"></a>第八步 可视化对比</h2><figure class="highlight reasonml"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br></pre></td><td class="code"><pre><code class="hljs reasonml">f_UMAP_more &lt;- <span class="hljs-keyword">function</span>(sObject, lc_group.by, lc_reduction=<span class="hljs-string">&quot;umap&quot;</span>)&#123;<br>    res &lt;- (<span class="hljs-constructor">DimPlot(<span class="hljs-params">sObject</span>, <span class="hljs-params">reduction</span> = <span class="hljs-params">lc_reduction</span>, <span class="hljs-params">group</span>.<span class="hljs-params">by</span> = <span class="hljs-params">lc_group</span>.<span class="hljs-params">by</span>[1], <span class="hljs-params">label</span> = T, <span class="hljs-params">repel</span> = T, <span class="hljs-params">label</span>.<span class="hljs-params">size</span> = 6)</span> + <br>    labs(title = lc_group.by<span class="hljs-literal">[<span class="hljs-number">1</span>]</span>))<br>    <span class="hljs-keyword">for</span>(lc_i <span class="hljs-keyword">in</span> <span class="hljs-number">2</span>:length(lc_group.by))&#123;<br>        res &lt;- res/<br>        (<span class="hljs-constructor">DimPlot(<span class="hljs-params">sObject</span>, <span class="hljs-params">reduction</span> = <span class="hljs-params">lc_reduction</span>, <span class="hljs-params">group</span>.<span class="hljs-params">by</span> = <span class="hljs-params">lc_group</span>.<span class="hljs-params">by</span>[<span class="hljs-params">lc_i</span>], <span class="hljs-params">label</span> = T, <span class="hljs-params">repel</span> = T, <span class="hljs-params">label</span>.<span class="hljs-params">size</span> = 6)</span> + <br>        labs(title = lc_group.by<span class="hljs-literal">[<span class="hljs-identifier">lc_i</span>]</span>))<br>    &#125;<br>    res<br>&#125;<br><br>f<span class="hljs-constructor">_UMAP_more(<span class="hljs-params">scRNA</span>, <span class="hljs-params">c</span>(&#x27;<span class="hljs-params">hM1_hmca_class</span>&#x27;, &#x27;<span class="hljs-params">hM1_class</span>&#x27;, &#x27;<span class="hljs-params">hmca_class</span>&#x27;, &#x27;<span class="hljs-params">manual_1</span>&#x27;)</span>)<br><br>f<span class="hljs-constructor">_UMAP_more(<span class="hljs-params">sc_4</span>, <span class="hljs-params">c</span>(&#x27;<span class="hljs-params">hM1_hmca_class</span>&#x27;, &#x27;<span class="hljs-params">hM1_class</span>&#x27;, &#x27;<span class="hljs-params">hmca_class</span>&#x27;, &#x27;<span class="hljs-params">manual_1</span>&#x27;)</span>)<br></code></pre></td></tr></table></figure>
                
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